Treatment Switching: statistical and decision making challenges and approaches

Objectives: Treatment switching refers to the situation in a randomised controlled trial where patients switch from their randomly assigned treatment onto an alternative. Often, switching is from the control group onto the experimental treatment. In this instance, a standard intention-to-treat analysis does not identify the true comparative effectiveness of the treatments under investigation. We aim to describe statistical methods for adjusting for treatment switching in a comprehensible way for non-statisticians, and to summarise views on these methods expressed by stakeholders at the 2014 Adelaide International Workshop on Treatment Switching in Clinical Trials. Methods: We describe three statistical methods used to adjust for treatment switching: marginal structural models, two-stage adjustment, and rank preserving structural failure time models. We draw upon discussion heard at the Adelaide International Workshop to explore the views of stakeholders on the acceptability of these methods. Results: Stakeholders noted that adjustment methods are based on assumptions, the validity of which may often be questionable. There was disagreement on the acceptability of adjustment methods, but consensus that when these are used, they should be justified rigorously. The utility of adjustment methods depends upon the decision being made and the processes used by the decision-maker. Conclusions: Treatment switching makes estimating the true comparative effect of a new treatment challenging. However, many decision-makers have reservations with adjustment methods. These, and how they affect the utility of adjustment methods, require further exploration. Further technical work is required to develop adjustment methods to meet real world needs, to enhance their acceptability to decision-makers

Treatment switching in cancer trials: Issues and proposals

Objectives: Treatment switching occurs when patients in a randomized clinical trial switch from the treatment initially assigned to them to another treatment, typically from the control to experimental treatment. This study discusses the issues this raises and possible approaches to addressing them in trials of cancer drugs. Methods: Stakeholders from around the world were invited to a 1.5-day Workshop in Adelaide, Australia. This study attempts to capture the key points from the discussion and the perspectives of the various stakeholder groups, but is not a formal consensus statement. Results: Treatment switching raises challenging ethical issues with arguments for and against allowing it. It is increasingly common in cancer drug trials and presents challenges for the interpretation of results by regulators, clinicians, patients, and payers. Proposals are offered for good practice in the design, management, and analysis of trials and wider development programs for cancer drugs in which treatment switching has occurred or is likely to. Recommendations are also offered for further action to improve understanding of the importance and challenges of treatment switching and to promote agreement between key stakeholders on guidelines and other steps to address these challenges. Conclusions: The handling of treatment switching in trials is of concern to all stakeholders. On the basis of the discussions at the Adelaide International Workshop, there would appear to be common ground on approaches to addressing treatment switching in cancer trials and scope for the development of formal guidelines to inform the work of regulators, payers, industry, trial designers and other stakeholders

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

The environmental stress cracking of polyethylenes-- a fracture mechanics approach

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Peripheral neural responses to cochlear stimulation via electrically evoked compound action potentials (ECAPs) of differing pulse distance and interphase gap

These data contain minimum and maximum Current Levels (CLs) that evoke ECAPs within the recoverable ECAP dynamic range for 52 electrodes of 10 cochlear implant users. <div><br></div><div>Data were collected with stimulus Interphase Gap (IPG) settings of 8 and 25 µS, and Pulse Distance (PD) settings of 25 and 40 µS, for each electrode. <div><br></div><div>These data are paired with four-interval-forced-choice behavioural thresholds for stimuli delivered at 40, 500, 1000, and 2000 pps (pulses per second), with IPG 8 and PD 25 µS.</div><div><br></div><div>These data were collected for the purpose of determining whether there is a correlation between the effect of changing PD/IPG on ECAP amplitude, and the slope of behavioural thresholds across pps conditions.</div></div><div><br></div><div>This dataset was utilised in </div><div><br></div><div>>Smale, N. (2015) <i>ECAP measures predict cochlear implant behavioural thresholds</i> (Master’s thesis). The University of Melbourne.</div><div>>McKay, C. M., & Smale, N. (2017). The relation between ECAP measurements and the effect of rate on behavioral thresholds in cochlear implant users. <i>Hearing research</i>, <i>346</i>, 62-70.</div

Can Greenlip ('Haliotis laevigata') Abalone Breeding Programs Tolerate Fluctuations in Reproductive Performance?

Fluctuations in reproductive performance (i.e., spawning success, hatching rate, larval survival) are a common occurrence in abalone breeding programs, in particular during the early stages of their development. Such fluctuations affect the numbers of families available for progeny testing and selection, and can have consequences for genetic gains and inbreeding. We used stochastic computer simulations to understand how genetic gains and levels of inbreeding are affected when greenlip ('Haliotis laevigata') breeding programs encounter varying severity and frequency of reproductive failure. We simulated breeding programs for greenlip abalone with both conservative and aggressive selection approaches over 35 y (10 generations). Without reproductive failure, genetic improvements of 36%-55% could be achieved after 10 y of selection in a single trait in a commercial abalone breeding program with a conservative selection approach, and gains of twice that could be achieved with a selection approach that allowed high rates of inbreeding. A conservative selection approach would be sustainable even at high rates of reproductive failure, whereas a more aggressive approach would lead to nearly twice the recommended level of inbreeding. It was concluded that breeding programs for greenlip abalone may be buffered against unexpected fluctuations in reproductive performance if the selection approach is chosen strategically